Model based process control methods are typically deployed for flexible operation and improved productivity of a number of processes such as those related to chemicals, polymers, natural gas processing, pharmaceuticals, microelectronics, and pulp and paper industries. These processes often require product grade transitions to fulfill customer needs and to reap benefits of market dynamics.
Because such a flexible mode of operation is required, many processes need to be transitioned and regulated over a broad range of operating conditions. Because the dynamics of such processes change significantly during such transitions, the underlying dynamic models used in the controller need to be updated. It is desired that this updating result in the deployment of high fidelity models which will help to consistently improve productivity, flexible operability, safety, and profitability of the operating units.
Model Predictive Control (MPC) technology is quite popular in refineries where the process usually operates at steady state and the process dynamics can be adequately represented by linear models. However, there are important instances in other adjacent industries (such as those involving chemicals, polymers, natural gas processing, pharmaceuticals, microelectronics, and pulp and paper) where linear models do not adequately represent the process dynamics. These processes are either highly nonlinear at a particular steady state (e.g., pH control) or involve frequent process transitions from one operating point to another (e.g., grade transition in polymer industries, etc.).
It is highly desirable to regulate these processes at an operating point and to transition them from one operating point to another in an optimal and controlled manner. Such controlled and optimal transitions can deliver many economic incentives such as reduced transition times, reduced amounts of off-spec products, optimal utility consumptions, etc. To achieve controlled and optimal regulation and transition of these processes, nonlinear model predictive control is more suited to address the issue of process nonlinearity.
The nonlinear model predictive control that is recommended for regulation of such processes uses either nonlinear dynamic models or successively linearized dynamic models of the processes. However, the nonlinear dynamic models that are used are generally very complex, difficult, and expensive to build because they are based on the first principles (underlying mass, energy and momentum conservation equations) governing the process. Moreover, nonlinear model predictive control has to solve a suitably formulated optimization problem, which is well known to those skilled in the art, in real time at every control execution interval. The use of complex nonlinear models for nonlinear model predictive control can lead to numerical and computational issues. In some cases, the solution of the optimization problem can become infeasible or non-optimal.
An alternative approach that is attractive and that potentially solves such problems is to decompose the nonlinear dynamics into multiple linear models, and then switch smoothly among the models based on the current state of the process. In such a case, the nonlinear model predictive control optimization problem based on linearized models becomes better structured and can be solved quickly and in real time. However, this approach requires the building of a library of linear dynamic models spanning the expected range of operation.
As a practical matter, the identification of the models to be used in the control is a data driven procedure. In general, model identification requires the application of properly designed perturbation signals that perturb the nonlinear process in the expected range of operation. Measurements of the manipulated variables (MVs), disturbance variables (DVs), and controlled variables (CVs) of the process during perturbation are collected as the process output data and are subjected to data based modeling.
It is highly important to keep the process within safety limits during such perturbations. Data based model identification is important to the success of nonlinear model predictive control. For the identification of multiple models, model identification as proposed herein should address such issues as (i) the selection of the number of local models, (ii) the development of the local models, (iii) the switching strategy to be implemented in switching between the models when the models are deployed online, and/or (iv) the closed loop stability when the models are switched.
Various approaches of model de-composition of nonlinear dynamic processes have been attempted. One approach has been proposed by Wojsznis, et al. U.S. Patent Application Publication No. US 2005/0149209) “Adaptive Multivariable Process Controller Using Model Switching and Attribute Interpolation.” The approach used by Wojsznis is to perform interpolation between local model parameters based on some heuristics and model validity measures. However, the heuristic based approach is inadequate for a wide variety of applications. This approach also assumes homogeneity of the local models and linear interpolation of the model parameters, an assumption that is not always valid.
An alternative approach is proposed by Narendra K., and Balakrishnan J. in “Adaptive control using multiple models”, IEEE Trans. on Auto. Cont., 42(2), 171-187, (1997). However, this approach assumes availability of knowledge of various operating regions, their ranges, and their centers, an assumption that is again not always valid.
Gomez Skarmeta, A., F., Delgado M., and Vila M., A., have proposed a fuzzy classification based multi model decomposition approach in “About the use of fuzzy clustering techniques for fuzzy model identification”, Fuzzy sets and Systems, 106, 179-188, (1999). However, this approach does not consider stability in selecting the optimal number of local models.
Galan et al., in “Gap Metric Concept and Implications for Multilinear Model-Based Controller Design,” Ind. Eng. Chem. Res., Vol. 42, pp. 2189-2197, (2003), have applied the stability analysis in selecting the optimal number of local models in their paper. However, in this approach, the local models are not obtained via data based modeling but are developed using linearization of first principle based model.
Moreover, none of the above approaches has considered a smooth switching strategy for the selected local models.
The present invention overcomes one or more of these or other problems.